{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先导入相关的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import csv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后处理csv文件。将x坐标，y坐标，type分别存入三个list当中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'dataset_circles.csv'\n",
    "X_point = []\n",
    "Y_point = []\n",
    "types = []\n",
    "with open(filename) as f:\n",
    "    reader = csv.reader(f)\n",
    "    # 读取一行，下面的reader中已经没有该行了\n",
    "    head_row = next(reader)\n",
    "    X_point.append(head_row[0])\n",
    "    Y_point.append(head_row[1])\n",
    "    types.append(head_row[2])\n",
    "    for row in reader:\n",
    "        # 行号从2开始\n",
    "        X_point.append(row[0])\n",
    "        Y_point.append(row[1])\n",
    "        types.append(row[2])\n",
    "x = np.array(X_point)\n",
    "y = np.array(Y_point)\n",
    "point_type = np.array(types)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    plt.scatter(x[i],y[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
